---
title: "JMU paper dashboard"
output: 
  flexdashboard::flex_dashboard:
    # orientation: columns
    # vertical_layout: fill
    storyboard: true
    social: menu
    source_code: embed
    theme: readable

---

```{r setup, include=TRUE, echo=FALSE, message=FALSE, warning=FALSE}
library(flexdashboard)
library(here)
library(kableExtra)
library(tidyverse, quietly=T)
library(forcats, quietly=T)
library(cowplot, quietly=T)    # publication-ready theme for ggplot2
library(scales, quietly=T)
library(DT, quietly=T)

# library(crosstalk)
# library(data.table,quietly=T)
# library(htmltools)
# library(htmlTable,quietly=T)

# Source: http://lenkiefer.com/2017/01/22/a-guide-to-building-an-interactive-flexdashboard/
# Source: https://beta.rstudioconnect.com/jjallaire/htmlwidgets-showcase-storyboard/htmlwidgets-showcase-storyboard.html

```


```{r all_data, echo=FALSE}
load(file = here("data", "all_data.rda"))

n_md <- nlevels(all_data$md_desc)
n_papers = nrow(all_data)

assessment_type <- c("NO ASSESSMENT", "USABILITY/UX", "EFFECT AND USABILITY/UX", "EFFECT")
assessment_type_n <- sapply(assessment_type, FUN = function(x) {nrow(filter(all_data, val_type == x))})

n_assessment_no <- assessment_type_n[[1]]
n_assessment_yes <- n_papers - n_assessment_no 

# Keep the same order of mental disorder for all charts 
temp_for_order <- 
    all_data %>% 
    group_by(md_id, md_desc) %>%
    summarise(number_cases = n()) %>%
    mutate(proportion = number_cases/n_papers) %>% 
    arrange(desc(proportion), md_id) 

# convert to factor to retain sorted order of mental disorders 
temp_for_order$md_desc <- factor(temp_for_order$md_desc, levels=unique(temp_for_order$md_desc))  

# Save ordered mental disorders
md_all_ordered <- levels(temp_for_order$md_desc)


# Adjust labels length for figures production, based on the order of MD given in 'md_all_ordered'
md_lbl_production <- c(
  "Depressive disorders", 
  "Various disorders",
  "Anxiety disorders",
  "Substance-related and\n addictive disorders",
  "Schizophrenia spectrum and\n other psychotic disorders",
  "Trauma and\n stressor-related disorders",
  "Suicidal behavior disorder/\nnonsuicidal self-injury",
  "Comorbid disorders",
  "Bipolar and related disorders",
  "Obsessive-compulsive and\n related disorders",
  "Neurodevelopmental disorders",
  "Feeding and eating disorders",
  "Sleep-wake disorders",
  "Personality disorders",
  "Major and mild\n neurocognitive disorders")    


default_palette <- c("NO ASSESSMENT"="#AF8DC3", "USABILITY/UX"="#D9F0D3", "EFFECT AND USABILITY/UX"="#7FBF7B", "EFFECT"="#1B7837")
```

### Table 1. Distribution of assessment types

```{r tab1, echo=FALSE}
data_tbl_assess <- 
    all_data %>%
    select(val_type) %>%
    group_by(val_type) %>%
    summarise(n = n()) %>%
    mutate(n_lbl = if_else(val_type == "NO ASSESSMENT", 
                           paste0(n, "/",n_papers, " (", round(n/n_papers*100,1), "%)*"),
                           paste0(n, "/",n_assessment_yes, " (", round(n/n_assessment_yes*100,1), "%)†")))

data_tbl_rct <- 
    all_data %>%
    select(val_type, val_edrct) %>%
    filter(val_edrct == "RCT") %>%
    group_by(val_type, val_edrct) %>%
    summarise(n = n()) %>%
    ungroup() %>%
    mutate(n_papers_rct = sum(n),
           n_lbl = paste0(n, "/",n_papers_rct," (", round(n/n_papers_rct*100,1), "%)‡"))

n_papers_rct <- data_tbl_rct$n_papers_rct[1] 
  
data_tbl_pilotrct <- 
    all_data %>%
    select(val_type, val_edrct) %>%
    filter(val_edrct == "PILOT RCT") %>%
    group_by(val_type, val_edrct) %>%
    summarise(n = n()) %>%
    ungroup() %>%
    mutate(n_papers_pilot = sum(n),
           n_lbl = paste0(n, "/",n_papers_pilot, " (", round(n/n_papers_pilot*100,1), "%)‡"))

n_papers_pilot <- data_tbl_pilotrct$n_papers_pilot[1]

#### Special case: there is no USABILITY/UX assessment with Pilot RCT. Add case
data_tbl_pilotrct <- 
  data_tbl_pilotrct %>%
  add_row(val_type = assessment_type[2],
          val_edrct = "PILOT RCT",
          n = 0,
          n_papers_pilot	= n_papers_pilot,
          n_lbl = paste0(n, "/",n_papers_pilot," (", round(n/n_papers_pilot*100,1), "%)‡"))

data_tbl_assesstype <-
  data_tbl_assess %>%
  full_join(data_tbl_rct, by="val_type", suffix=c("",".rct")) %>%
  full_join(data_tbl_pilotrct, by="val_type", suffix=c("",".pilot")) %>%
  select(-n, -val_edrct, -n.rct, -val_edrct.pilot, -n.pilot)

data_tbl_assesstype$val_type <- forcats::fct_relevel(data_tbl_assesstype$val_type, assessment_type) 

# Aggregate the sum of the theee types of assessment 
data_tbl_assesstype <- 
  data_tbl_assesstype %>% 
  arrange(val_type) %>%
  add_row(.after = 1, 
          val_type="ASSESSMENT (total)", 
          n_lbl=paste0(n_assessment_yes, "/",n_papers, " (", round(n_assessment_yes/n_papers*100,1), "%)*"), 
          n_lbl.rct=paste0(n_papers_rct, "/",n_assessment_yes, " (", round(n_papers_rct/n_assessment_yes*100,1), "%)†"),
          n_lbl.pilot=paste0(n_papers_pilot, "/",n_assessment_yes , " (", round(n_papers_pilot/n_assessment_yes*100,1), "%)†"))

# Table output with Kable
options(knitr.kable.NA = 'N/A')
data_tbl_assesstype %>%
    select(`Assessment type` = val_type,
           `ALL - N (%)` = n_lbl,
           `RCT - N (%)` = n_lbl.rct,
           `PILOT RCT - N (%)` = n_lbl.pilot) %>%
    knitr::kable(format="html", escape = T, booktabs = TRUE,
          caption = "Distribution of assessment types.") %>%
    kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
    row_spec(1, bold = T, color = "white", background = default_palette[["NO ASSESSMENT"]]) %>%
    row_spec(3, bold = T, color = "black", background = default_palette[["USABILITY/UX"]]) %>%
    row_spec(4, bold = T, color = "black", background = default_palette[["EFFECT AND USABILITY/UX"]]) %>%
    row_spec(5, bold = T, color = "white", background = default_palette[["EFFECT"]]) %>%
    add_indent(3:5) %>%
    column_spec(1, bold = T) %>%
    footnote(symbol =
               c(paste0("Percentage based on the total number of studies (N=",n_papers,")"),
                 paste0("Percentage based on the number of studies with an assessment (N=",n_assessment_yes,")"),
                 paste0("Percentage based on the number of RCT (N=",n_papers_rct,") and Pilot RCT studies (N=",n_papers_pilot,"), respectively")))



```

***



### Figure 2: Temporal trend of amount of articles published and assessment type.

```{r fig2_tempdist, fig.width=10, fig.height=22, fig.asp=1}
cols <- c("md", "md_id", "md_desc", "val_type", "year")

data_plot_tempdist <- 
    all_data %>%
    select(cols)

data_plot_tempdist$val_type <- forcats::fct_relevel(data_plot_tempdist$val_type, assessment_type)


data_plot_tempdist <- 
    data_plot_tempdist %>%
    group_by(year, val_type) %>%  # first create counts for each group
    summarise(number_cases = n()) %>%
    mutate(total_cases_per_year = sum(number_cases),
           proportion = number_cases/total_cases_per_year,
           proportion_lbl = paste0(round(proportion*100,1), "%"))

# Not run
# display.brewer.all()
# display.brewer.pal(n = 6, name = "PRGn")
# brewer.pal(n = 6, name = "PRGn")[2:5]
pal <- default_palette

plot_tempdist_stakedbarchart <- 
    data_plot_tempdist %>%
        ggplot(aes(x=year, y=number_cases, fill=val_type)) + 
        geom_bar(stat="identity") +
        geom_text(aes(label=paste0(number_cases, " - ", proportion_lbl)), size=2.7, position=position_stack(vjust = 0.5)) +
        stat_summary(fun.y = sum, aes(label=..y.., group=year), geom = "text", vjust=-.2) +
        labs(title="Temporal trend of articles coloured by assessment type", 
             x="Year", 
             y="Number of papers - Percentage of papers [%] per year") +
        scale_fill_manual(values = pal) +
        scale_x_continuous(breaks = seq(2013,2018, by=1)) +
        scale_y_continuous(breaks = seq(0, 60, by=10)) +
        guides(fill=guide_legend(title="Assessment type", nrow=4)) + # modify legend title
        theme_minimal() +
        # Add legend in the top-left corner of the plot
        theme(legend.title = element_text(size=11), 
              # legend.justification = c('right', 'top'),
              legend.position = c(0.20, 0.75),
              legend.background = element_rect(color = "darkgray", size = 0.5, linetype ="solid"),
              legend.key = element_blank()) +
        # Change the line type and color of axis lines
        theme(axis.line = element_line(colour = "darkgray", size = 0.5, linetype = "solid")) +
        theme(panel.grid.minor = element_blank()) +
        theme(panel.background = element_blank()) +
        theme(plot.margin=unit(rep(20, 4), "pt"))

plot_tempdist_stakedbarchart

```

***

- It shows the temporal distribution of research over the study period 2013-2018, along with their reported assessment type. Overall, we observed a positive evolution of the amount of research over time, steadily increasing from only few (7) articles in 2013 to a much larger amount (60) in 2018.
 
- Looking at the distribution of assessment types over time, we observe an overall slow proportional increase of studies with an assessment.

- Regarding the type of assessment, we observe that only a fifth of the articles with assessment (22/113, 19.5%) focus specifically on the effect of intervention on clinical symptomatology (drak green).


### Figure 3: Distribution of articles per mental disorder, categorized according to assessment type. 

```{r fig3_barchart, echo=FALSE, fig.width=10,fig.asp=0.65}
cols <- c("md", "md_id", "md_desc", "val_type")

data_plot_barchart <- 
    all_data %>%
    select(cols)

data_plot_barchart <- 
    data_plot_barchart %>% 
        group_by(md_id, md_desc, val_type) %>%  # first create counts for each group
        summarise(number_cases = n()) %>%
        mutate(proportion = number_cases/n_papers) %>% 
        ungroup() %>%
        group_by(md_id) %>%
        mutate(total_cases = sum(number_cases),
               proportion_sum = sum(proportion),
               proportion_lbl = paste0(round(proportion_sum*100,1), "%")) %>% 
        ungroup() %>%
        mutate(lbl = paste0(round(number_cases/total_cases*100,1), "%")) %>% 
        arrange(desc(proportion_sum), md_id)

# To keep "unified" order in plot.
data_plot_barchart$md_desc <- forcats::fct_relevel(data_plot_barchart$md_desc, md_all_ordered)  
data_plot_barchart$val_type <- forcats::fct_relevel(data_plot_barchart$val_type, assessment_type)

pal <- default_palette

lbls <- distinct(data_plot_barchart, md_desc, proportion_sum, proportion_lbl)

top_proportion <- sum(lbls[1:6, "proportion_sum"]) 
top_lbl <- paste0(round(top_proportion*100,1), "%")


# Compute aggregated results for assessment types
data_table <-
    data_plot_barchart %>%
    group_by(val_type) %>%
    summarise(total_asstype = sum(number_cases)) %>%
    mutate(lbl = paste0(round(total_asstype/n_papers*100,1), "%"))

data_table[1] <- c("No assessment", "Usability/UX", "Effect + Usability/UX", "Effect")

lbl_subtitle <- "Aggregated results of assessment types: "
for (i in 1:nrow(data_table)) {
  lbl_subtitle <- paste0(lbl_subtitle, data_table[i,1], " ", data_table[i,2], "/", n_papers, 
                     " (" ,data_table[i,3], ")")
  if (i==nrow(data_table))
      lbl_subtitle <- paste0(lbl_subtitle, ".")
  else
      lbl_subtitle <- paste0(lbl_subtitle, ", ")  
}


plot_barchart <- 
    data_plot_barchart %>%
      ggplot(aes(x=md_desc, y=proportion, fill=val_type)) + 
        geom_bar(stat="identity") +
        labs(title="Distribution per mental disorder and assessment type", 
             x="Mental disorders", 
             y="Number of papers and total percentage", 
             caption="Source: authors") + 
        geom_point(aes(y=proportion_sum), size=6, color="white", show.legend = F) +  
        geom_text(aes(label=number_cases), size=2.5, position=position_stack(vjust = 0.4)) +
        # Percentatge inside point
        annotate("text", x = lbls$md_desc, y = lbls$proportion_sum,
                 label = lbls$proportion_lbl, color = "black", size=2, hjust = 0.4, vjust = 0.2) +
        # Arrow to indidate  Top6 mental disorders 
        annotate("text", x = "Schizophrenia spectrum and other psychotic disorders", y = .16,
                 label = top_lbl, color = "black", size = 3, hjust = -0.1, vjust = 1.2) +
        geom_segment(aes(x = "Schizophrenia spectrum and other psychotic disorders", 
                         xend = "Substance-related and addictive disorders", 
                         y = .18, 
                         yend = .18),
                         arrow = arrow(length = unit(0.5,"cm")), color = "black") +
        geom_segment(aes(x="Schizophrenia spectrum and other psychotic disorders", 
                         y=0.11, 
                         xend="Schizophrenia spectrum and other psychotic disorders", 
                         yend=0.18), color="black") +
        coord_flip() +
        scale_fill_manual(name="Assessment type", values = pal) +
        scale_x_discrete(labels = md_lbl_production) +
        scale_y_continuous(expand=c(0,0), labels=scales::percent_format(accuracy=1), breaks=seq(0, 0.21, by=0.02), limits=c(0, 0.21)) +
        # Which legend to show
        guides(fill=guide_legend(title="Assessment type", nrow=4)) + # modify legend title
        theme_minimal()  +
        theme(legend.title = element_text(size=11),
              legend.position = c(0.70, 0.75),
              legend.background = element_rect(color = "darkgray", size = 0.5, linetype ="solid"),
              legend.key = element_blank()) +
        # Change the line type and color of axis lines
        theme(axis.line = element_line(colour = "darkgray", size = 0.5, linetype = "solid")) +
        theme(panel.grid.major = element_blank()) + 
        theme(panel.grid.minor = element_blank()) +
        theme(panel.background = element_blank()) +
        theme(plot.margin=unit(rep(20, 4), "pt"))

plot_barchart


```

***

- It shows the number of studies per mental disorder, ranked in ascending order and subcategorized according to the type of assessment.

- `r lbl_subtitle`


### New table. Type assessment per mental disorder category

```{r stats_assesstype_md, echo=FALSE}

# Table output with DT
data_plot_barchart %>%
  select(`Mental Disorder` = md_desc,
           `Type of assessment` = val_type,
           `Relative N` = number_cases,
           `Relative %`= lbl) %>%
  datatable(rownames = FALSE, 
            filter = "top",
            class = "table-bordered table-condensed hover",
            extensions = c("Buttons", "RowGroup"),
            options = list(
               pageLength = 8, #autoWidth = TRUE,
               dom = 'Bfrtip',  # https://datatables.net/reference/option/dom
               buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
               rowGroup=list(dataSrc=0)
            )) %>%
    formatStyle('Relative N',
      background = styleColorBar(c(0, data_plot_barchart$number_cases), 'lightblue'),
      backgroundSize = '98% 55%',
      backgroundRepeat = 'no-repeat',
      backgroundPosition = 'center')

```

***

To complement figure 3, some percentages are discussed in the body of the article which are included in the following table.



### New table. Characteristics of included studies/app 

```{r tab_apps, echo=FALSE}
data_all_apps <- 
  all_data %>%
  select(app_name, md_desc, year, id, fmw_cbt, fmw_wav, fmw_pos, fmw_tra, ux_gui, ux_gesture, ux_voice, ux_text) %>%
  filter(!is.na(app_name)) %>%
  arrange(md_desc, year)


# a custom table container
sketch <- 
  htmltools::withTags(table(
  class = 'display',
  thead(
    tr(
      th(rowspan = 2, 'APP NAME'),
      th(rowspan = 2, 'MD CATEGORY'),
      th(rowspan = 2, 'YEAR'),
      th(rowspan = 2, 'REF'),
      th(colspan = 4, 'PSY FRAMEWORK'),
      th(colspan = 3, 'UX')
    ),
    tr(
      lapply(c('CBT', 'WAV', 'TRA', 'POS'), th),
      lapply(c('GES', 'VOI', 'TXT'), th)
    )
  )
))

# sketch

data_all_apps %>%
  select(`APP NAME` = app_name,
         `MD CATEGORY` = md_desc,
         `YEAR` = year,
         `REF`= id,
         `CBT` = fmw_cbt,
         `WAV` = fmw_wav,
         `TRA` = fmw_tra,
         `POS` = fmw_pos,
         `GES` = ux_gesture,
         `VOI` = ux_voice,
         `TXT` = ux_text) %>%
  datatable(container = sketch, rownames = FALSE, 
            filter = "top",
            class = "table-bordered table-condensed hover",
            extensions = "Buttons",
            options = list(
               pageLength = 6, #autoWidth = TRUE,
               dom = 'Bfrtip',  # https://datatables.net/reference/option/dom
               buttons = c('copy', 'csv', 'excel', 'pdf', 'print')
            )) %>%
    formatStyle('CBT',
      backgroundColor = styleEqual(c("YES", "NO"), c('red', ''))) %>%
    formatStyle('WAV',
      backgroundColor = styleEqual(c("YES", "NO"), c('orange', ''))) %>%
    formatStyle('TRA',
      backgroundColor = styleEqual(c("YES", "NO"), c('yellow', ''))) %>%
    formatStyle('POS',
      backgroundColor = styleEqual(c("YES", "NO"), c('blue', ''))) %>%
    # formatStyle('GUI',
    #   backgroundColor = styleEqual(c("YES", "NO"), c('lightgreen', ''))) %>%
    formatStyle('GES',
      backgroundColor = styleEqual(c("YES", "NO"), c('lightgreen', ''))) %>%
    formatStyle('VOI',
      backgroundColor = styleEqual(c("YES", "NO"), c('lightgreen', ''))) %>%
    formatStyle('TXT',
      backgroundColor = styleEqual(c("YES", "NO"), c('lightgreen', '')))
```  

***

Response to issue [Characteristics of included studies/app](https://github.com/cgranell/apps-treatment-mental-disorders/issues/1)

- The table attempts to recreate the aesthetics of Table 1 in [Evaluation Strategies for HCI Toolkit Research, CHI2018](https://dl.acm.org/citation.cfm?doid=3173574.3173610)
- FIXME: Which variables per app are left?
- FIXME: all studies (w/o appname) vs studies with appname?
- FIXME: same app in different years. How to manage it?
- FIXME: ux_gui is not significative. Group all "ux" variables under "UX headings" ans do a second table as in the example paper?


### Figure 4: Distribution of articles published for the top 6 mental disorders over time.

```{r fig4_linechart, echo=FALSE, fig.width=12, fig.height=14, fig.asp=0.65}

n_top <- 6
md_top <- md_all_ordered[1:n_top]
md_top_lbls <- md_lbl_production[1:n_top] 
  
data_plot_topmd <- 
    all_data %>%
      filter(md_desc %in% md_top) %>%
      group_by(md_desc, year) %>%
      summarise(number_cases = n()) %>%
      ungroup() %>%
      mutate(md_desc = factor(md_desc)) %>%
      arrange(desc(number_cases))

#' Add mental disorders with zero cases
for (y in 2013:2018) {
    md_year <- filter(data_plot_topmd, year==y) %>% select(md_desc)
    md_dif <- setdiff(md_top, md_year$md_desc)
    if (length(md_dif) > 0) {
        for (md in md_dif) {
            data_plot_topmd <- add_row(data_plot_topmd, md_desc=md, year=y, number_cases=0)
        }
    }
}


data_plot_topmd$md_desc <- forcats::fct_relevel(data_plot_topmd$md_desc, md_top)
brks <- levels(data_plot_topmd$md_desc) 


plot_topmd <-
    data_plot_topmd %>%
        ggplot(aes(x=year, y=number_cases, group=md_desc)) +
        geom_vline(aes(xintercept = 2017), color="lightgray", linetype = "dashed", size = 0.5) +
        annotate("rect", xmin = 2017, xmax = 2018, ymin = -Inf, ymax = +Inf, fill = "lightgray", alpha = 0.2) +
        geom_line(aes(color=md_desc), size=2.5, alpha=.4) +
        geom_point(shape=21, size=7, color="darkgray", fill="white") +
        labs(#title="Distribution top mental disorders per year", 
             #subtitle = paste0("Top mental disorders (", top_lbl,")"),
             x="Year", 
             y="# of papers", 
             caption="Source: authors") + 
        scale_color_brewer(name="Mental disorders", palette="Set2", breaks=brks) + # labels=md_top_lbls
        geom_text(aes(label = number_cases), color= "black", size=3) +
        scale_y_continuous(breaks=seq(0,8,by=1)) +
        theme_minimal() +
        # Legend: Top-Left Inside the Plot"
        theme(legend.justification = c('left', 'top'),
              legend.position=c(0.05, 0.95),  
              legend.background = element_rect(color = "darkgray", size = 0.5, linetype ="solid"),
              legend.key = element_blank()) +
        theme(panel.grid.major = element_blank()) + 
        theme(panel.grid.minor = element_blank()) +
        theme(panel.background = element_blank()) +
        theme(plot.margin=unit(rep(20, 4), "pt")) +
        # Change the line type and color of axis lines
        theme(axis.line = element_line(colour = "darkgray", size = 0.5, linetype = "solid"))

plot_topmd
```

***
A _line chart_ to show the number of papers per mental disorder and year. 


### Table 2: Mental disorders and the studies targeting them.


```{r tab2, echo=FALSE}

unite_paper_ids <- function(mentaldisorder, appname) {
    if (!is.na(appname)) {
        all_data %>% 
            filter(md_desc == mentaldisorder & app_name==appname) %>%
            arrange(year) %>%
            select(id) %>%
            as_vector() %>%
            stringr::str_c(collapse = ";")    
    } else {
         all_data %>% 
            filter(md_desc == mentaldisorder & is.na(app_name)) %>%
            arrange(year) %>%
            select(id) %>%
            as_vector() %>%
            stringr::str_c(collapse = ";")  
    }
}

data_kp_apps <- 
    all_data %>%
    group_by(md_desc, app_name) %>%
    summarise(number_apps = n()) %>% 
    arrange(number_apps, md_desc)

data_kp_apps <- 
    data_kp_apps %>%
    add_column(ids = purrr::map2(data_kp_apps$md_desc, data_kp_apps$app_name, unite_paper_ids))

data_kp_apps <- 
    data_kp_apps %>%
    add_column(app_ids = paste0(data_kp_apps$app_name, " (", data_kp_apps$ids, ")"))

data_kp_apps <- 
    data_kp_apps %>%
    group_by(md_desc) %>%
    summarise(app_ids_merge = paste0(app_ids, collapse = ", ")) 

data_kp_apps$md_desc <- forcats::fct_relevel(data_kp_apps$md_desc, md_all_ordered)


# Table output with DT 
data_kp_apps %>%
    arrange(desc(md_desc)) %>%
    select(`Mental Disorder` = md_desc,
           `References by app` = app_ids_merge) %>%
  datatable(rownames = FALSE, 
            filter = "top",
            class = "table-bordered table-condensed hover",
            extensions = "Buttons",
            options = list(
               pageLength = 7, #autoWidth = TRUE,
               dom = 'Bfrtip',  # https://datatables.net/reference/option/dom
               buttons = c('copy', 'csv', 'excel', 'pdf', 'print')
            ))  %>%
      formatStyle('Mental Disorder',  fontWeight = 'bold')
``` 


***
A tabular, compact distribution of papers (apps) per mental disorder, grouping the references per app. The number(s) in brackets next to the app name is the reference(s) in which the app is mentioned. 

- In the table, these references are internal identifiers instead. 

- NA = app name not available/not mentioned.


### Figure 5: Bubble plot representing technology-related dimensions versus mental disorders.

```{r fig5_feat, echo=FALSE, warning=FALSE}
cols = c("id", "md_id", "md_desc", 
         "feat_use", "feat_promp", "feat_soc", "feat_hcp", "feat_learn", 
         "feat_prog", "feat_ca", "feat_ass", "feat_vr", "feat_ar", "feat_pers", "feat_game",
         "app_name", "year")

cols_feat = c("feat_use", "feat_promp", "feat_soc", "feat_hcp", "feat_learn", 
              "feat_prog", "feat_ca", "feat_ass", "feat_vr", "feat_ar", "feat_pers", "feat_game")

data_kp_feat <- 
    all_data %>%
    select(cols) %>%
    gather(cols_feat, key="tech_type",value="tech_value") %>%
    mutate(cat = "Software features") %>%
    filter(tech_value == "YES") %>%
    group_by(md_desc, tech_type, cat)  %>%
    summarise(number_cases = n())  %>%
    ungroup() # required to add_row() 


# For totals in final bubble plot
data_kp_feat_n <-
    data_kp_feat %>%
    group_by(tech_type) %>%
    summarise(n = sum(number_cases)) %>%
    arrange(desc(n))
                  

md_feat <- unique(data_kp_feat$md_desc)
md_dif <- setdiff(md_all_ordered, md_feat)
if (length(md_feat) > 0) {
    for (md in md_dif) {
        # Added "feat_use" (or any value) to avoid NA in the "tech_type" variable. Nothing is drawn in the plot
        data_kp_feat <- add_row(data_kp_feat, md_desc=md, tech_type="feat_use", cat="Software features")
    }
}

data_kp_feat$md_desc <- forcats::fct_relevel(data_kp_feat$md_desc, md_all_ordered)

```

```{r fig5_sens, echo=FALSE, warning=FALSE}
cols = c("id", "md_id", "md_desc", 
         "sens_acc", "sens_gyr", "sens_gps", "sens_mic", "sens_cam",
         "app_name", "year")

cols_sens <- c("sens_acc", "sens_gyr", "sens_gps", "sens_mic", "sens_cam")
data_kp_sens <-
    all_data %>%
    select(cols) %>%
    gather(cols_sens, key="tech_type", value="tech_value") %>%
    mutate(cat = "Built-In sensors") %>%
    filter(tech_value == "YES") %>%
    group_by(md_desc, tech_type, cat) %>%
    summarise(number_cases = n()) %>%
    ungroup() # required to add_row() 


# For totals in final bubble plot
data_kp_sens_n <-
    data_kp_sens %>%
    group_by(tech_type) %>%
    summarise(n = sum(number_cases)) %>%
    arrange(desc(n))
                  

md_sens <- unique(data_kp_sens$md_desc)
md_dif <- setdiff(md_all_ordered, md_sens)
if (length(md_dif) > 0) {
    for (md in md_dif) {
        # Added "sens_mic" (or any other values) to avoid NA in the "tech_type" variable. Nothing is drawn in the plot
        data_kp_sens <- add_row(data_kp_sens, md_desc=md, tech_type="sens_mic", cat="Built-In sensors")
    }
}

data_kp_sens$md_desc <- forcats::fct_relevel(data_kp_sens$md_desc, levels=md_all_ordered)


```



```{r fig5_anal, echo=FALSE, warning=FALSE}
cols = c("id", "md", "md_id", "md_desc", 
         "anal_ml", "anal_beh", "anal_act", "anal_sp", 
         "app_name", "year")

cols_anal = c("anal_ml", "anal_beh", "anal_act", "anal_sp")

data_kp_anal <- 
    all_data %>%
    select(cols) %>%
    gather(cols_anal, key="tech_type",value="tech_value") %>%
    mutate(cat = "Analytics") %>%
    filter(tech_value == "YES") %>%
    group_by(md_desc, tech_type, cat)  %>%
    summarise(number_cases = n())  %>%
    ungroup() # required to add_row() 

# For totals in final bubble plot
data_kp_anal_n <-
    data_kp_anal %>%
    group_by(tech_type) %>%
    summarise(n = sum(number_cases)) %>%
    arrange(desc(n))

md_anal <- unique(data_kp_anal$md_desc)
md_dif <- setdiff(md_all_ordered, md_anal)
if (length(md_anal) > 0) {
    for (md in md_dif) {
        # Added "anal_ml" (or any value) to avoid NA in the "tech_type" variable. Nothing is drawn in the plot
        data_kp_anal <- add_row(data_kp_anal, md_desc=md, tech_type="anal_ml", cat="Analytics")
    }
}

data_kp_anal$md_desc <- forcats::fct_relevel(data_kp_anal$md_desc, levels=md_all_ordered)  

```


```{r fig5_alltogether, echo=FALSE, warning=FALSE, fig.width=15, fig.height=14, fig.asp=0.65}

data_kp_all <-
    bind_rows(data_kp_feat, data_kp_sens, data_kp_anal)

data_kp_all$tech_type <- as_factor(data_kp_all$tech_type)

# Replace existing factors levels for figure production
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="feat_learn"] <- "Learning"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="feat_prog"] <- "Progress"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="feat_pers"] <- "Personalization"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="feat_ass"] <- "Assessment"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="feat_promp"] <- "Prompting"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="feat_hcp"] <- "Health Care Provider \nCommunication"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="feat_use"] <- "In-Situ Use"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="feat_soc"] <- "Social"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="feat_ca"] <- "Context-Awareness"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="feat_game"] <- "Game"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="feat_vr"] <- "Virtual Reality"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="feat_ar"] <- "Augmented Reality"

levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="sens_mic"] <- "Microphone"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="sens_gps"] <- "GPS"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="sens_cam"] <- "Camera"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="sens_acc"] <- "Accelerometer"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="sens_gyr"] <- "Gyroscope"

levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="anal_ml"] <- "Machine Learning"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="anal_act"] <- "Activity Analysis"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="anal_beh"] <- "Behavioral Analysis"
levels(data_kp_all$tech_type)[levels(data_kp_all$tech_type)=="anal_sp"] <- "Spatial Analysis"

cols_ordered <-  c("Learning", "Progress", "Personalization", "Assessment", "Prompting", "Health Care Provider \nCommunication", 
                   "In-Situ Use", "Social", "Context-Awareness", "Game",  "Virtual Reality", "Augmented Reality", 
                   "Microphone", "GPS", "Camera", "Accelerometer", "Gyroscope",  
                   "Machine Learning", "Activity Analysis", "Behavioral Analysis", "Spatial Analysis") 
dims_ordered <- c("Software features", "Built-In sensors", "Analytics")

data_kp_all$tech_type <- forcats::fct_relevel(data_kp_all$tech_type, cols_ordered)

data_kp_all_n <-
    bind_rows(data_kp_feat_n, data_kp_sens_n, data_kp_anal_n)

data_kp_all_n <-
    data_kp_all_n %>%
    mutate(tech_label = cols_ordered)


data_kp_all$cat <- factor(data_kp_all$cat)
data_kp_all$cat <- forcats::fct_relevel(data_kp_all$cat, dims_ordered)
brks <- levels(factor(data_kp_all$cat))

# Color per dimension: 
# Software features "#FC8D62"; Built-In sensors "#66C2A5"; Types of analyses "#8DA0CB"
lbls_colors <- 
    ifelse(unique(data_kp_all$tech_type) %in% c("Learning", "Progress", "Personalization", "Assessment", "Prompting", "Health Care Provider \nCommunication", "In-Situ Use", "Social", "Context-Awareness", "Game",  "Virtual Reality", "Augmented Reality"), "#FC8D62",
           ifelse(unique(data_kp_all$tech_type) %in% c("Microphone", "GPS", "Camera", "Accelerometer", "Gyroscope"), "#66C2A5",
           "#8DA0CB"))
               
kp_bubblechart <- 
    data_kp_all %>%
    ggplot(aes(x=md_desc, y=tech_type, colour=cat)) +
    geom_point(aes(size=number_cases), alpha=1, na.rm = TRUE)+#, show.legend = FALSE) +
    geom_text(aes(label=number_cases), colour="black", size=3, na.rm = TRUE) +
    scale_size_area(max_size=18) +
    coord_flip() +
    labs(#title="Mental disorders vs Technology", 
         #subtitle = "Technology-related characteristics are ranked in each dimension",
         x="Mental disorders", 
         y="Technology-related characteristics", 
         caption="Source: authors") + 
    scale_color_manual(name="Dimensions", breaks=brks,
                       values = c("Software features"="#FC8D62","Built-In sensors"="#66C2A5", "Analytics"="#8DA0CB")) +
    # Which legend to show
    guides(colour="legend",size = "none") +
    theme_minimal()  +
    theme(axis.text.x=element_text(angle=60, size=11, hjust=1, color=lbls_colors),
          axis.text.y=element_text(size=11)) +
    # Legend: Top-Right Inside the Plot") 
    theme(legend.title = element_text(size=9),
          legend.justification = c('right', 'top'),
          legend.position=c(1, 0.90),
          legend.background = element_rect(color = "darkgray", size = 0.5, linetype ="solid"),
          legend.key = element_blank()) +
    # Change the line type and color of axis lines
    theme(axis.line = element_line(colour = "darkgray", size = 0.5, linetype = "solid")) +
    theme(panel.grid.major.x = element_blank()) + 
    theme(panel.grid.minor = element_blank()) +
    theme(panel.background = element_blank()) +
    theme(plot.margin=unit(rep(20, 4), "pt")) 

# Add annotations: total of columns
kp_bubblechart <-
    kp_bubblechart +
    annotate("rect", xmin = 14.40, xmax = 14.75, ymin = 0.5, ymax = 12.4, 
             fill = "#FC8D62", alpha = 0.6) +
    annotate("rect", xmin = 14.40, xmax = 14.75, ymin = 12.6, ymax = 17.4, 
             fill = "#66C2A5", alpha = 0.6) +
    annotate("rect", xmin = 14.40, xmax = 14.75, ymin = 17.6, ymax = 21.5, 
             fill = "#8DA0CB", alpha = 0.6)
    
for (i in 1:nrow(data_kp_all_n)) {
     kp_bubblechart <-
        kp_bubblechart +
        annotate("text", x = "Personality disorders", y = data_kp_all_n$tech_label[i], label = data_kp_all_n$n[i],
                 color = "white", fontface="bold", size=3, hjust = 0.4, vjust = -3)
}

# Add rectangles to highlight group of bubbles
kp_bubblechart <-
    kp_bubblechart +
    annotate("rect", xmin = 0.5, xmax = 11.5, ymin = 0.5, ymax = 7.5, 
             fill = "lightgray", alpha = 0.2, linetype="dashed") +
    annotate("rect", xmin = 0.5, xmax = 5.5, ymin = 0.5, ymax = 9.5, 
             fill = "lightgray", alpha = 0.2)

kp_bubblechart
```


***
- software features - orange; built-in sensors - green; analytics - blue  

- Bubble size corresponds with amount of articles.

- It is a _bubble plot_ that shows the distribution of papers per mental disorder (bubble size) and technology-related characteristics grouped by dimensions (bubble color): software features, built-in sensors, and analytics. Technology-related characteristics are ranked in each dimension.